cpu、gpu 安装框架pytorch，cntk,theano及测试

一，cpu 下安装

tensorflow

conda env list

source activate tensorflow

直接安装相应版本

python

import tensorflow as tf

tf.version 1.11.0

keras 直接安装

conda env list

source activate keras

import keras 2.2.2

print(keras.version)

import tensorflow as tf

tf.version

pytorch

import torch

print(torch.version)

print(torch.cuda.device_count())

print(torch.cuda.is_available())

cntk

/root/anaconda3/bin/conda env list

source activate cntk-py35

python 3.5.6

export PATH=/root/anaconda3/bin:$PATH

python -c "import cntk; print(cntk.version)"

theano

caffe2

python 3.6.9

import caffe2

安装

conda create -n caffe2 python=3.6

conda activate caffe2

conda install pytorch-nightly-cpu -c pytorch -n caffe2

python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"

报错:

pip install protobuf

pip install future

参考官网安装即可

gpu

tensorflow-gpu:1.11.0 python 3.5

export PATH=/root/anaconda3/bin:$PATH

source activate tensorflow

keras

export PATH=/root/anaconda3/bin:$PATH

conda env list

source activate keras

python3.5

nvidia-docker run -it --rm pytorch-gpu:1.1.0 /bin/bash

pytorch

[root@191ddd30d4ae /]# python

Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)

[GCC 7.3.0] on linux

Type "help", "copyright", "credits" or "license" for more information.

import torch

print(torch.version)

1.1.0

print(torch.cuda.device_count())

1

print(torch.cuda.is_available())

True

cntk

source activate cntk-py35 python3.5

python -c "import cntk; print(cntk.version)"

2.4

theano

gpu-theano-in-use:1.0.4 python2.7

source activate theano

python test.py

import theano

/root/anaconda3/envs/theano/lib/python2.7/site-packages/theano/gpuarray/dnn.py:184: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to a version >= v5 and <= v7.

warnings.warn("Your cuDNN version is more recent than "

Using cuDNN version 7603 on context None

Mapped name None to device cuda: GeForce GTX 960M (0000:01:00.0)

theano.version

u'1.0.4'

https://www.jianshu.com/p/4cc75a79dce9

Linux下安装miniconda

在官网下载miniconda3

执行：bash Miniconda3-latest-Linux-x86_64.sh

－vim ~/.bashrc

－export PATH=~/anaconda3/bin:$PATH

－source ~/.bashrc

创建虚拟环境并安装theano

基于python2.7创建一个名为theano的环境

conda create --name theano python=2.7

进入虚拟环境： source activate theano

－使用conda安装：conda install numpy scipy mkl

pip install parameterized

conda install theano pygpu

－使用pip安装：pip install Theano


测试参考官网文档

caffe2

看官网文档安装

https://caffe2.ai/docs/getting-started.html?platform=ubuntu&configuration=compile

https://blog.csdn.net/qq_35451572/article/details/79428167

cmake

-DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-9.0

-DCUDNN_ROOT_DIR=/usr/local/cuda

To check if Caffe2 build was successful

python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"

To check if Caffe2 GPU build was successful

This must print a number > 0 in order to use Detectron

python -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'

参考

https://blog.csdn.net/Yan_Joy/article/details/70241319

https://www.nvidia.com/en-gb/data-center/gpu-accelerated-applications/caffe2/

https://blog.csdn.net/qq_35451572/article/details/79428167

https://blog.csdn.net/qq_16525279/article/details/79724728

https://blog.csdn.net/y_f_raquelle/article/details/83278953

https://www.cnblogs.com/nanzhao/p/9596844.html

附:conda常用

1. conda env list 或 conda info -e 查看当前存在哪些虚拟环境

2. conda update conda 检查更新当前conda

3. conda update --all 更新本地已安装的包

4. conda create -n your_env_name python=X.X（2.7、3.6等） anaconda 命令创建python版本为X.X、名字为your_env_name的虚拟环境。your_env_name文件可以在Anaconda安装目录envs文件下找到。

5. Windows: activate your_env_name(虚拟环境名称) 激活虚拟环境

6. conda install -n your_env_name [package] 安装package到your_env_name中

7. linux: source deactivate Windows: deactivate 关闭虚拟环境

8. conda remove -n your_env_name(虚拟环境名称) --all 删除虚拟环境

9. conda remove --name your_env_name package_name 删除环境中的某个